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Rmd	573a477	reneeisnowhere	2023-06-01	Updateing supplement 1 seoan chi results
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Rmd	acbd0a8	reneeisnowhere	2023-05-22	updates on GWAS enrichment
Rmd	e8c82ec	reneeisnowhere	2023-05-18	adding other_analysis and genes of interest log2cpm

’

Data set comparison order:

Knowles data:
meQTLS(aka K4 supp)
reQTLS(aka K5 supp)
ArrGWAS
HFGWAS
CADGWAS
GTEx enrichment
Seaone 2019
Supplemental 1 (408 genes)
Supplemental 4 (54 genes)

#Pairwise Data enrichment ## Knowles Comparison data:

Determining the genetic basis of anthracycline-cardiotoxicity by molecular response QTL mapping in induced cardiomyocytes David A Knowles, Courtney K Burrows†, John D Blischak, Kristen M Patterson, Daniel J Serie, Nadine Norton, Carole Ober, Jonathan K Pritchard, Yoav Gilad

Knowles \(~~et~ al.~\) eLife 2018;7:e33480. DOI: https://doi.org/10.7554/eLife.33480 My first question was about transcription response at the 24 hour mark with my treatments. 3 hour RNA-seq had low levels of DEGs,so my focus is at 24 hours. This also happens to be when the Knowles paper collected their RNA-seq data

Supplementary 4 contains a list of 518 SNPs within 1 Mb of TSS, which had a detectable marginal effect on expression (5% FDR). When converted from ensembl gene id to entrez gene id, my list of unique Entrezgeneids = 521. I will call these meSNPs for marginal effect snps. In the meSNPs, 503 are within my DEG of 14084. Using an adj. P value of 0.05, There are 199/6864 in 24 hour daunorubicin, 184/6516 in 24 hour doxorubicin, 182/6202 in 24 hour epirubicin, 30/1327 in 24 hour mitoxantrone and 0 in Trastuzumb

Supplementary 5 contains a list of 376 response eQTLs (reQTLs). These are variants that were associated with modulation of transcriptomic response to doxorubicin treatment. After database name conversion, I have 377 unique Entregene ids. Of the reQTLs list, 374 are within my DEG of 14084. Using an adj. P value of 0.05, There are 187/6864 in 24 hour daunorubicin, 180/6516 in 24 hour doxorubicin, 176/6202 in 24 hour epirubicin, 40/1327 in 24 hour mitoxantrone and 0 in Trastuzumb.

Count of genes in each treatment by using adj. P value of <0.05
time	id	n	K4	K5
24_hours	Daunorubicin	6864	199	187
24_hours	Doxorubicin	6516	184	180
24_hours	Epirubicin	6202	172	176
24_hours	Mitoxantrone	1327	30	40

Count of genes in each treatment by total expressed genes
time	id	n	K4	K5
24_hours	Daunorubicin	14084	503	374
24_hours	Doxorubicin	14084	503	374
24_hours	Epirubicin	14084	503	374
24_hours	Mitoxantrone	14084	503	374
24_hours	Trastuzumab	14084	503	374

meQTLs (mininmally expressed QTLS) chi-squared pvalues
id	time	pvalue
Daunorubicin	24_hours	0.0000338
Daunorubicin	3_hours	0.0018777
Doxorubicin	24_hours	0.0000113
Doxorubicin	3_hours	0.9232984
Epirubicin	24_hours	0.0000074
Epirubicin	3_hours	0.2189641
Mitoxantrone	24_hours	0.0086490
Mitoxantrone	3_hours	0.6853643

reQTLS in knowles data (reQTLS) chi-squared pvalues
id	time	pvalue
Daunorubicin	24_hours	0.6576304
Daunorubicin	3_hours	0.7387684
Doxorubicin	24_hours	0.4965954
Doxorubicin	3_hours	1.0000000
Epirubicin	24_hours	0.2539396
Epirubicin	3_hours	0.5705567
Mitoxantrone	24_hours	0.4445513
Mitoxantrone	3_hours	0.3946217

ArrGWAS to 24 hour DEG genes p < 0.05

24 hour data set

Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 24 hour GWAS
id	sigcount	ARR	ARRcount
Daunorubicin	notsig	no	7169
Daunorubicin	notsig	y	51
Daunorubicin	sig	no	6795
Daunorubicin	sig	y	69
Doxorubicin	notsig	no	7512
Doxorubicin	notsig	y	56
Doxorubicin	sig	no	6452
Doxorubicin	sig	y	64
Epirubicin	notsig	no	7827
Epirubicin	notsig	y	55
Epirubicin	sig	no	6137
Epirubicin	sig	y	65
Mitoxantrone	notsig	no	12650
Mitoxantrone	notsig	y	107
Mitoxantrone	sig	no	1314
Mitoxantrone	sig	y	13
Trastuzumab	notsig	no	13964
Trastuzumab	notsig	y	120

3 hour data set

Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 24 hour GWAS
id	sigcount	ARR	ARRcount
Daunorubicin	notsig	no	7169
Daunorubicin	notsig	y	51
Daunorubicin	sig	no	6795
Daunorubicin	sig	y	69
Doxorubicin	notsig	no	7512
Doxorubicin	notsig	y	56
Doxorubicin	sig	no	6452
Doxorubicin	sig	y	64
Epirubicin	notsig	no	7827
Epirubicin	notsig	y	55
Epirubicin	sig	no	6137
Epirubicin	sig	y	65
Mitoxantrone	notsig	no	12650
Mitoxantrone	notsig	y	107
Mitoxantrone	sig	no	1314
Mitoxantrone	sig	y	13
Trastuzumab	notsig	no	13964
Trastuzumab	notsig	y	120

chi square test ARR

chi_funarr <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(ARR=if_else(ENTREZID %in%Arr_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(ARR, sigcount)$p.value) 
print("after performing chi square test between DEgenes, and non DE genes")

[1] "after performing chi square test between DEgenes, and non DE genes"

chi_funarr

# A tibble: 8 × 3
# Groups:   id [4]
  id           time      pvalue
  <fct>        <chr>      <dbl>
1 Daunorubicin 24_hours 0.0662 
2 Daunorubicin 3_hours  0.0246 
3 Doxorubicin  24_hours 0.142  
4 Doxorubicin  3_hours  1.00   
5 Epirubicin   24_hours 0.0313 
6 Epirubicin   3_hours  0.644  
7 Mitoxantrone 24_hours 0.708  
8 Mitoxantrone 3_hours  0.00409

HFGWAS

24 hours HF

Significant (adj. P value of <0.05) and non-sig gene counts in HFhythmia GWAS
id	sigcount	HF	HFcount
Daunorubicin	notsig	no	7209
Daunorubicin	notsig	y	11
Daunorubicin	sig	no	6842
Daunorubicin	sig	y	22
Doxorubicin	notsig	no	7556
Doxorubicin	notsig	y	12
Doxorubicin	sig	no	6495
Doxorubicin	sig	y	21
Epirubicin	notsig	no	7868
Epirubicin	notsig	y	14
Epirubicin	sig	no	6183
Epirubicin	sig	y	19
Mitoxantrone	notsig	no	12728
Mitoxantrone	notsig	y	29
Mitoxantrone	sig	no	1323
Mitoxantrone	sig	y	4
Trastuzumab	notsig	no	14051
Trastuzumab	notsig	y	33

3 hours HF

Significant (adj. P value of <0.05) and non-sig gene counts in Three hour HFhythmia GWAS
id	sigcount	HF	HFcount
Daunorubicin	notsig	no	13498
Daunorubicin	notsig	y	31
Daunorubicin	sig	no	553
Daunorubicin	sig	y	2
Doxorubicin	notsig	no	14035
Doxorubicin	notsig	y	33
Doxorubicin	sig	no	16
Epirubicin	notsig	no	13831
Epirubicin	notsig	y	33
Epirubicin	sig	no	220
Mitoxantrone	notsig	no	13993
Mitoxantrone	notsig	y	33
Mitoxantrone	sig	no	58
Trastuzumab	notsig	no	14051
Trastuzumab	notsig	y	33

chi square test HF

chi_funhf <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(HF=if_else(ENTREZID %in%HF_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(HF, sigcount)$p.value) 
print("after performing chi square test between DEgenes, and non DE genes")

[1] "after performing chi square test between DEgenes, and non DE genes"

chi_funhf

# A tibble: 8 × 3
# Groups:   id [4]
  id           time     pvalue
  <fct>        <chr>     <dbl>
1 Daunorubicin 24_hours 0.0589
2 Daunorubicin 3_hours  0.858 
3 Doxorubicin  24_hours 0.0674
4 Doxorubicin  3_hours  1.00  
5 Epirubicin   24_hours 0.164 
6 Epirubicin   3_hours  0.983 
7 Mitoxantrone 24_hours 0.816 
8 Mitoxantrone 3_hours  1.00

CAD GWAS

24 hour data set

Significant (adj. P value of <0.05) and non-sig gene counts in CAD GWAS
id	sigcount	CAD	CADcount
Daunorubicin	notsig	no	7107
Daunorubicin	notsig	y	113
Daunorubicin	sig	no	6748
Daunorubicin	sig	y	116
Doxorubicin	notsig	no	7447
Doxorubicin	notsig	y	121
Doxorubicin	sig	no	6408
Doxorubicin	sig	y	108
Epirubicin	notsig	no	7762
Epirubicin	notsig	y	120
Epirubicin	sig	no	6093
Epirubicin	sig	y	109
Mitoxantrone	notsig	no	12547
Mitoxantrone	notsig	y	210
Mitoxantrone	sig	no	1308
Mitoxantrone	sig	y	19
Trastuzumab	notsig	no	13855
Trastuzumab	notsig	y	229

3 hour data set

Significant (adj. P value of <0.05) and non-sig gene counts in 3 hour CAD GWAS
id	sigcount	CAD	CADcount
Daunorubicin	notsig	no	13317
Daunorubicin	notsig	y	212
Daunorubicin	sig	no	538
Daunorubicin	sig	y	17
Doxorubicin	notsig	no	13839
Doxorubicin	notsig	y	229
Doxorubicin	sig	no	16
Epirubicin	notsig	no	13643
Epirubicin	notsig	y	221
Epirubicin	sig	no	212
Epirubicin	sig	y	8
Mitoxantrone	notsig	no	13798
Mitoxantrone	notsig	y	228
Mitoxantrone	sig	no	57
Mitoxantrone	sig	y	1
Trastuzumab	notsig	no	13855
Trastuzumab	notsig	y	229

chi square test CAD

chi_funCAD <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(CAD=if_else(ENTREZID %in%CAD_geneset$entrezgene_id,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(CAD, sigcount)$p.value) 
print("after performing chi square test between DEgenes, and non DE genes")

[1] "after performing chi square test between DEgenes, and non DE genes"

chi_funCAD

# A tibble: 8 × 3
# Groups:   id [4]
  id           time     pvalue
  <fct>        <chr>     <dbl>
1 Daunorubicin 24_hours 0.604 
2 Daunorubicin 3_hours  0.0105
3 Doxorubicin  24_hours 0.836 
4 Doxorubicin  3_hours  1.00  
5 Epirubicin   24_hours 0.304 
6 Epirubicin   3_hours  0.0351
7 Mitoxantrone 24_hours 0.636 
8 Mitoxantrone 3_hours  1.00

GTEx V8 heart-left ventrical egenes

####chi square

Significant (adj. P value of <0.05) and non-sig gene counts in Arrhythmia 24 hour GWAS
id	time	sigcount	no	y
Daunorubicin	24_hours	notsig	866	6354
Daunorubicin	24_hours	sig	797	6067
Daunorubicin	3_hours	notsig	1628	11901
Daunorubicin	3_hours	sig	35	520
Doxorubicin	24_hours	notsig	920	6648
Doxorubicin	24_hours	sig	743	5773
Doxorubicin	3_hours	notsig	1662	12406
Doxorubicin	3_hours	sig	1	15
Epirubicin	24_hours	notsig	953	6929
Epirubicin	24_hours	sig	710	5492
Epirubicin	3_hours	notsig	1651	12213
Epirubicin	3_hours	sig	12	208
Mitoxantrone	24_hours	notsig	1503	11254
Mitoxantrone	24_hours	sig	160	1167
Mitoxantrone	3_hours	notsig	1660	12366
Mitoxantrone	3_hours	sig	3	55
Trastuzumab	24_hours	notsig	1663	12421
Trastuzumab	3_hours	notsig	1663	12421

[1] "after performing chi square test between DEgenes, and non DE genes"

# A tibble: 8 × 3
# Groups:   id [4]
  id           time        pvalue
  <fct>        <chr>        <dbl>
1 Daunorubicin 24_hours 0.498    
2 Daunorubicin 3_hours  0.0000556
3 Doxorubicin  24_hours 0.175    
4 Doxorubicin  3_hours  0.763    
5 Epirubicin   24_hours 0.251    
6 Epirubicin   3_hours  0.00454  
7 Mitoxantrone 24_hours 0.802    
8 Mitoxantrone 3_hours  0.172

Seaone 2019

Seoane, Jose Chromatin gene comparison: comes from supp data NAT. MED 2019 ### 24 hours in Pairwise with supplemental data 1

Significant (adj. P value of <0.05) and non-sig gene counts in Seoane geneset
id	notsig_no	notsig_y	sig_no	sig_y
Daunorubicin	7065	155	6689	175
Doxorubicin	7407	161	6347	169
Epirubicin	7717	165	6037	165
Mitoxantrone	12483	274	1271	56
Trastuzumab	13754	330	NA	NA

# A tibble: 4 × 2
  id               pvalue
  <fct>             <dbl>
1 Daunorubicin 0.128     
2 Doxorubicin  0.0771    
3 Epirubicin   0.0314    
4 Mitoxantrone 0.00000326

3 hours data Pairwise

Significant (adj. P value of <0.05) and non-sig gene counts in 3 hours Seoane geneset
id	notsig_no	notsig_y	sig_no	sig_y
Daunorubicin	13227	302	527	28
Doxorubicin	13738	330	16	NA
Epirubicin	13551	313	203	17
Mitoxantrone	13698	328	56	2
Trastuzumab	13754	330	NA	NA

chi square test Seaone

##remove Trastuzumab in order to perform chi square tests by time and drug between  DE and non DE enrichment
chi_fun <-  toplistall %>% 
  mutate(id = as.factor(id)) %>%
  dplyr::filter(id!="Trastuzumab") %>% 
  mutate(sigcount = if_else(adj.P.Val <0.05,'sig','notsig'))%>%
  mutate(chrom=if_else(ENTREZID %in%chrom_genes,"y","no")) %>% 
  group_by(id,time) %>% 
  summarise(pvalue= chisq.test(chrom, sigcount)$p.value) 
print("after performing chi square test between DEgenes, and non DE genes")

[1] "after performing chi square test between DEgenes, and non DE genes"

chi_fun

# A tibble: 8 × 3
# Groups:   id [4]
  id           time          pvalue
  <fct>        <chr>          <dbl>
1 Daunorubicin 24_hours 0.128      
2 Daunorubicin 3_hours  0.0000332  
3 Doxorubicin  24_hours 0.0771     
4 Doxorubicin  3_hours  1.00       
5 Epirubicin   24_hours 0.0314     
6 Epirubicin   3_hours  0.000000346
7 Mitoxantrone 24_hours 0.00000326 
8 Mitoxantrone 3_hours  0.902

Supplemental 4 Seoane 3 hours followed by 24 hours

List of Seoane Supplemental 4 genes
entrez	gene	pval.exp	pval.anthr	pval.expAnth	adjpval
11176	BAZ2A	0.0020064	0.0000768	0.0004553	0.1299006
10284	SAP18	0.0013141	0.0000648	0.0006081	0.1299006
8819	SAP30	0.0023742	0.0000576	0.0007455	0.1299006
23522	KAT6B	0.0050327	0.0001601	0.0012776	0.1343691
7786	MAP3K12	0.0062822	0.0001296	0.0014816	0.1343691
2146	EZH2	0.0075650	0.0001626	0.0020478	0.1343691
4297	KMT2A	0.0096126	0.0001301	0.0023292	0.1343691
79913	ACTR5	0.0087568	0.0001883	0.0035210	0.1373866
8242	KDM5C	0.0139853	0.0001783	0.0036176	0.1373866
51780	KDM3B	0.0155602	0.0001675	0.0039239	0.1373866
6872	TAF1	0.0105527	0.0001952	0.0043619	0.1447734
23135	KDM6B	0.0074796	0.0001950	0.0047811	0.1514738
6877	TAF5	0.0233826	0.0002047	0.0067329	0.1624738
23030	KDM4B	0.0239951	0.0004270	0.0069023	0.1624738
64324	NSD1	0.0164702	0.0003839	0.0069286	0.1624738
79885	HDAC11	0.0256039	0.0002383	0.0071964	0.1624738
10847	SRCAP	0.0174738	0.0003660	0.0077132	0.1624738
7404	UTY	0.0114041	0.0002112	0.0078450	0.1624738
51773	RSF1	0.0283587	0.0001948	0.0080182	0.1624738
5253	PHF2	0.0119978	0.0002989	0.0093089	0.1624738
9126	SMC3	0.0347884	0.0002127	0.0095907	0.1624738
3054	HCFC1	0.0317868	0.0003159	0.0097354	0.1624738
9734	HDAC9	0.0353794	0.0001985	0.0103307	0.1649465
53335	BCL11A	0.0063102	0.0004723	0.0105391	0.1649465
83444	INO80B	0.0255912	0.0003477	0.0112276	0.1701220
27350	APOBEC3C	0.0051330	0.0004220	0.0122160	0.1745980
6601	SMARCC2	0.0336512	0.0003435	0.0122745	0.1745980
1108	CHD4	0.0238388	0.0003994	0.0127656	0.1778779
8289	ARID1A	0.0492112	0.0004149	0.0146053	0.1870798
890	CCNA2	0.0444477	0.0004539	0.0147624	0.1870798
64151	NCAPG	0.0003946	0.0003956	0.0154184	0.1919043
10445	MCRS1	0.0185317	0.0003143	0.0162352	0.1977683
7150	TOP1	0.0468031	0.0003256	0.0175446	0.2072644
8110	DPF3	0.0612773	0.0004235	0.0182917	0.2124890
54531	MIER2	0.0244962	0.0004771	0.0198964	0.2273412
51409	HEMK1	0.0718548	0.0004890	0.0223436	0.2395917
27097	TAF5L	0.0450661	0.0003586	0.0237889	0.2512251
9739	SETD1A	0.0590016	0.0005136	0.0245980	0.2558930
6595	SMARCA2	0.0491644	0.0005485	0.0267793	0.2645703
9555	H2AFY	0.0852250	0.0004323	0.0277200	0.2645703
22823	MTF2	0.0823105	0.0005160	0.0278843	0.2645703
54556	ING3	0.0701823	0.0004542	0.0280892	0.2645703
10592	SMC2	0.0788583	0.0006366	0.0286097	0.2658792
8360	HIST1H4D	0.0801302	0.0004891	0.0300157	0.2715200
7528	YY1	0.1017709	0.0005254	0.0342873	0.2836505
9031	BAZ1B	0.1069563	0.0005045	0.0354054	0.2836505
51377	UCHL5	0.1048249	0.0005627	0.0372967	0.2954064
7799	PRDM2	0.0130131	0.0006154	0.0382200	0.2993182
6602	SMARCD1	0.1110653	0.0006993	0.0446426	0.3241241
8202	NCOA3	0.1179716	0.0006899	0.0454845	0.3251323
51564	HDAC7	0.1331938	0.0007507	0.0463305	0.3251323
26038	CHD5	0.0624026	0.0005717	0.0477023	0.3265622
79858	NEK11	0.1358428	0.0006363	0.0490482	0.3265622
10856	RUVBL2	0.1277997	0.0007652	0.0498579	0.3278390

[1] "These are the chisquare values from the 54 genes"

# A tibble: 8 × 3
# Groups:   id [4]
  id           time     pvalue
  <fct>        <chr>     <dbl>
1 Daunorubicin 24_hours 0.546 
2 Daunorubicin 3_hours  0.732 
3 Doxorubicin  24_hours 0.501 
4 Doxorubicin  3_hours  1.00  
5 Epirubicin   24_hours 0.320 
6 Epirubicin   3_hours  0.748 
7 Mitoxantrone 24_hours 0.0202
8 Mitoxantrone 3_hours  1.00

Mitoxantrone is significantly enriched at 24 hours in the 54 genes from supplemental 4 Seoane.

Using Baysian gene sets to look at enrichment of Seoane data

Seoane Supplemental 4

<environment: R_GlobalEnv>

Summary of genes from Cormotif that are also in Seoane Supp4
chrom	none	ER	TI	LR	NR
no	63	7482	5525	525	439
y	NA	22	20	3	5


    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list4[, c("NR", "LR")]
X-squared = 0.36327, df = 1, p-value = 0.5467


    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list4[, c("NR", "ER")]
X-squared = 6.3065, df = 1, p-value = 0.01203


    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list4[, c("NR", "TI")]
X-squared = 4.099, df = 1, p-value = 0.04291


    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list4[, c("ER", "TI")]
X-squared = 0.26698, df = 1, p-value = 0.6054


    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list4[, c("ER", "LR")]
X-squared = 0.47936, df = 1, p-value = 0.4887


    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list4[, c("TI", "LR")]
X-squared = 0.13763, df = 1, p-value = 0.7107

Seoane Supplemental 1

Summary of genes from Cormotif that are also in Seoane Supp1
chrom	none	ER	TI	LR	NR
no	61	7363	5406	514	410
y	2	141	139	14	34


    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list1[, c("NR", "LR")]
X-squared = 11.832, df = 1, p-value = 0.0005824


    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list1[, c("NR", "ER")]
X-squared = 62.351, df = 1, p-value = 2.873e-15


    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list1[, c("NR", "TI")]
X-squared = 37.066, df = 1, p-value = 1.142e-09


    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list1[, c("ER", "TI")]
X-squared = 5.6896, df = 1, p-value = 0.01707


    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list1[, c("ER", "LR")]
X-squared = 1.1741, df = 1, p-value = 0.2786


    Pearson's Chi-squared test with Yates' continuity correction

data:  chi_list1[, c("TI", "LR")]
X-squared = 0.003306, df = 1, p-value = 0.9541

R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] readxl_1.4.2       broom_1.0.3        kableExtra_1.3.4   sjmisc_2.8.9      
 [5] scales_1.2.1       ggpubr_0.6.0       cowplot_1.1.1      RColorBrewer_1.1-3
 [9] biomaRt_2.52.0     ggsignif_0.6.4     lubridate_1.9.2    forcats_1.0.0     
[13] stringr_1.5.0      dplyr_1.1.0        purrr_1.0.1        readr_2.1.4       
[17] tidyr_1.3.0        tibble_3.1.8       ggplot2_3.4.1      tidyverse_2.0.0   
[21] limma_3.52.4       workflowr_1.7.0   

loaded via a namespace (and not attached):
  [1] colorspace_2.1-0       ellipsis_0.3.2         sjlabelled_1.2.0      
  [4] rprojroot_2.0.3        XVector_0.36.0         fs_1.6.1              
  [7] rstudioapi_0.14        farver_2.1.1           bit64_4.0.5           
 [10] AnnotationDbi_1.58.0   fansi_1.0.4            xml2_1.3.3            
 [13] cachem_1.0.7           knitr_1.42             jsonlite_1.8.4        
 [16] dbplyr_2.3.1           png_0.1-8              compiler_4.2.2        
 [19] httr_1.4.5             backports_1.4.1        fastmap_1.1.1         
 [22] cli_3.6.0              later_1.3.0            htmltools_0.5.4       
 [25] prettyunits_1.1.1      tools_4.2.2            gtable_0.3.1          
 [28] glue_1.6.2             GenomeInfoDbData_1.2.8 rappdirs_0.3.3        
 [31] Rcpp_1.0.10            carData_3.0-5          Biobase_2.56.0        
 [34] cellranger_1.1.0       jquerylib_0.1.4        vctrs_0.5.2           
 [37] Biostrings_2.64.1      svglite_2.1.1          insight_0.19.0        
 [40] xfun_0.37              ps_1.7.2               rvest_1.0.3           
 [43] timechange_0.2.0       lifecycle_1.0.3        rstatix_0.7.2         
 [46] XML_3.99-0.13          getPass_0.2-2          zlibbioc_1.42.0       
 [49] vroom_1.6.1            hms_1.1.2              promises_1.2.0.1      
 [52] parallel_4.2.2         yaml_2.3.7             curl_5.0.0            
 [55] memoise_2.0.1          sass_0.4.5             stringi_1.7.12        
 [58] RSQLite_2.3.0          highr_0.10             S4Vectors_0.34.0      
 [61] BiocGenerics_0.42.0    filelock_1.0.2         GenomeInfoDb_1.32.4   
 [64] rlang_1.0.6            pkgconfig_2.0.3        systemfonts_1.0.4     
 [67] bitops_1.0-7           evaluate_0.20          labeling_0.4.2        
 [70] bit_4.0.5              processx_3.8.0         tidyselect_1.2.0      
 [73] magrittr_2.0.3         R6_2.5.1               IRanges_2.30.1        
 [76] generics_0.1.3         DBI_1.1.3              pillar_1.8.1          
 [79] whisker_0.4.1          withr_2.5.0            KEGGREST_1.36.3       
 [82] abind_1.4-5            RCurl_1.98-1.10        crayon_1.5.2          
 [85] car_3.1-1              utf8_1.2.3             BiocFileCache_2.4.0   
 [88] tzdb_0.3.0             rmarkdown_2.20         progress_1.2.2        
 [91] grid_4.2.2             blob_1.2.3             callr_3.7.3           
 [94] git2r_0.31.0           digest_0.6.31          webshot_0.5.4         
 [97] httpuv_1.6.9           stats4_4.2.2           munsell_0.5.0         
[100] viridisLite_0.4.1      bslib_0.4.2

Comparisons with other data sets

ERM

2023-06-02

ArrGWAS to 24 hour DEG genes p < 0.05

24 hour data set

3 hour data set

chi square test ARR

HFGWAS

24 hours HF

3 hours HF

chi square test HF

CAD GWAS

24 hour data set

3 hour data set

chi square test CAD

GTEx V8 heart-left ventrical egenes

Seaone 2019

3 hours data Pairwise

chi square test Seaone

Supplemental 4 Seoane 3 hours followed by 24 hours

Using Baysian gene sets to look at enrichment of Seoane data

Seoane Supplemental 4

Seoane Supplemental 1